3D Reconstruction of a Precast Concrete Bridge for Damage Inspection Using Images from Low-Cost Unmanned Aerial Vehicle
DOI:
https://doi.org/10.70028/dcea.v1i2.31Keywords:
UAS, UAV, VI, 3D-Reconstruction, Bridge AssessmentAbstract
Early damage detection in bridges is fundamental to their continued safety, and therefore of utmost importance to the bridge managers and policy makers. The traditional visual inspection which is the common practice for bridge inspection is inefficient, time-consuming, costly, risky, subjective and require the expertise of highly qualified inspectors. Consequently, the use of unmanned aerial systems (UASs) has gained significant attention in the area of bridge inspection. Most UAVs are quite expensive, ranging between $8000-$25000 for the best categories. It however requires the use of UAV equipped with high-cost sensors and longer battery duration, and adequate man power for real-time inspection. The expense of the UAV based inspection makes it unaffordable for many African countries. More recently, 3D models are increasingly deployed for image-based damage identification in bridges and other structures. The 3D models are constructed inform of a digital twin, and allow for computerized inspection using low-cost drones. This paper presents 3D Reconstruction approach for damage inspection and condition assessment of a bridge using images from low-cost unmanned aerial vehicle (UAV). The overall approach was illustrated in form of a case study on a precast concrete bridge. The 3D Reconstructed model of the bridge was virtually inspected to detect damages such as cracks, delamination, concrete deterioration, etc. The results showed that 3D reconstruction using low-cost UAV has great potential in its applications in bridge assessment.
Downloads
References
D. Aldana-Rodríguez, D. L. Ávila-Granados, and J. A. Villalba-Vidales, “Use of unmanned aircraft systems for bridge inspection: a review,” DYNA, vol. 88, no. 217, pp. 32–41, Apr.–Jun. 2021.
G. Morgenthal and N. Hallermann, “Quality assessment of Unmanned Aerial Vehicle (UAV) based visual inspection of structures,” Advances in Structural Engineering, vol. 17, no. 3, pp. 289–302, 2014, doi: http://dx.doi.org/10.1260/1369-4332.17.3.289
K. Chitanya and D. Umesh, “Visual inspection of concrete bridge,” International Journal of Innovations in Engineering Research and Technology, vol. 4, no. 3, Mar. 2017.
M. Aliyari, B. Ashrafi, and Y. Z. Ayele, “Hazards identification and risk assessment for UAV–assisted bridge inspections,” Structure and Infrastructure Engineering, 2021, doi: http://dx.doi.org/10.1080/15732479.2020.1858878
N. Ghosh et al., “Enhancing 3D reconstruction model by deep learning and its application in building damage assessment after earthquake,” Applied Sciences, vol. 12, p. 9790, 2022, doi: http://dx.doi.org/10.3390/app12199790
N. Gucunski et al., “Implementation of a fully autonomous platform for assessment of concrete bridge decks RABIT,” in Structures Congress, Reston, VA, 2015, pp. 367–378, doi: http://dx.doi.org/10.1061/9780784479117.032
E. S. Kocaman et al., “Monitoring the damage state of fiber reinforced composites using an FBG network for failure prediction,” Materials, vol. 10, no. 1, p. 32, 2017.
S. Yoon, G. Gwon, J. Lee, and H. Jung, “Three-dimensional image coordinate-based missing region of interest area detection and damage localization for bridge visual inspection using unmanned aerial vehicles,” Structural Health Monitoring, vol. 2020, pp. 1462–1475, 2020.
A. Mohan and S. Poobal, “Crack detection using image processing: A critical review and analysis,” Alexandria Engineering Journal, 2017, doi: http://dx.doi.org/10.1016/j.aej.2017.01.020
Y. Xu et al., “Identification framework for cracks on a steel structure surface by a restricted Boltzmann machines algorithm based on consumer-grade camera images,” Structural Control and Health Monitoring, vol. 25, no. 2, e2075, 2018, doi: http://dx.doi.org/10.1002/stc.2075
Y. Noh, D. Koo, Y. M. Kang, D. Park, and D. Lee, “Automatic crack detection on concrete images using segmentation via fuzzy C-means clustering,” in 2017 IEEE Int. Conf. Appl. Syst. Innov. (ICASI), Sapporo, 2017, pp. 877–880, doi: http://dx.doi.org/10.1109/ICASI.2017.7988574
S. Dorafshan, C. Coopmans, R. Thomas, and M. Maguire, “Deep learning neural networks for sUAS-assisted structural inspections: Feasibility and application,” IEEE ICUAS18, Dallas, TX, (In Press).
E. T. Bartezak, M. Bassier, and M. Vergauwan, “Case study for UAS-assisted bridge inspections,” Int. Arch. Photogramm. Remote Sens. Spatial Inf. Sci., vol. XLVIII-2/W3-2023, 2023, doi: http://dx.doi.org/10.5194/isprs-archives-XLVIII-2-W3-2023-33-202
T. Moons, L. Van Gool, and M. Vergauwen, “3D reconstruction from multiple images part 1: Principles,” Foundations and Trends in Computer Graphics and Vision, vol. 4, no. 4, pp. 287–404, 2010.
S. Dorafshan, M. Maguire, N. Hoffer, and C. Coopmans, Fatigue Crack Detection Using Unmanned Aerial Systems in Under-Bridge Inspection, RP 256, Boise: Idaho Dept. of Transportation, 2017.
H. Tariq, M. Ahmad, G. Ahmad, and M. Waleed, “Modeling and analysis of a concrete bridge using 3D reconstruction technique,” 2023, doi: http://dx.doi.org/10.1063/5.0154294
A. Jiponov, “Inspection and condition assessment of existing overpass, Sofia, Bulgaria,” 2023, doi: http://dx.doi.org/10.1002/cepa.2186
Y.-F. Liu, X. Nie, J.-S. Fan, and X.-G. Liu, “Image-based crack assessment of bridge piers using unmanned aerial vehicles and three-dimensional scene reconstruction,” Comput.-Aided Civ. Infrastructure Eng., vol. 35, no. 5, pp. 511–529, 2020, doi: http://dx.doi.org/10.1111/mice.12501
C. P. Bechlioulis, S. Heshmati-Alamdari, G. C. Karras, and K. J. Kyriakopoulos, “Robust image-based visual servoing with prescribed performance under field of view constraints,” IEEE Trans. Robot., vol. 35, no. 4, pp. 1063–1070, 2019.
Y. Lin-Yao, C. Si-Yuan, W. Xiao, Z. Jun, and W. Cheng-Hong, “Digital twins and parallel systems: State of the art, comparisons and prospect,” Acta Automatica Sinica, vol. 45, no. 11, pp. 2001–2031, 2019.
K. Chai et al., “Fusion of TLS and UAV photogrammetry data for post-earthquake 3D modeling of a cultural heritage church,” ISPRS Archives, vol. XLII-3-W4, p. 143, 2018, doi: http://dx.doi.org/10.5194/isprs-archives-XLII-3-W4-143-2018
P. Cosmin, M. Ali, O. Ulf, and T. U. Lucea, “Bridge inspection using aerial vehicles – A case study in Sweden,” Technical Report, Lulea Univ. of Technology, Sweden, 2021.
DJI, “DJI Terra,” 2019. [Online]. Available: https://enterprise.dji.com/dji-terra.
DJI, “DJI Fly,” 2019. [Online]. Available: https://www.dji.com/global/downloads/djiapp/dji-fly.
Downloads
Published
Issue
Section
License
Copyright (c) 2025 Bala Ismail Muhammad, Omoniyi Tope Moses, Oluwaseun Omoebamije, Abba-Gana Mohammed, Duna Samson (Author)
This work is licensed under a Creative Commons Attribution-NonCommercial 4.0 International License.
